Tutorials and notebooks

1- Fundamental components

Graphs manipulation
Random Variables
- Common API for Random Discrete Variables
  - DiscreteVariable
- Concrete classes for Random Discrete Variables
Tensor and Instantiation
- Instantiation
  - Instantiation
- Tensor
  - Tensor

2- Bayesian networks

Model for Bayesian Network
- BayesNet
Input/Output Formats for Bayesian Networks
- Supported Formats
  - BIFXML (Recommended for complete preservation)
  - Pickle (Alternative for complete preservation)
- Other Supported Formats
Inference
- Exact Inference
- Approximated Inference
Learning
- Class BNLearner
  - BNLearner
Classifier with Bayesian Networks
- Classifier using Bayesian networks
  - BNClassifier
Tools for Bayesian networks

3- Causality

Probabilistic causality
- Causal models and do-calculus
- Causel Effect estimation
  - Causal Effect Estimation
    - CausalEffectEstimation
Tools for causality
Causal Model
- CausalModel
Causal Inference (a.k.a. do-calculus)
Other functions for causal inference
Causal Effect Estimation
- CausalEffectEstimation

4- Other graphical models

Influence Diagram and LIMIDS
- Model for Decision in PGM
  - InfluenceDiagram
- Inference for Influence Diagram
  - ShaferShenoyLIMIDInference
Credal Network
- CN Model
  - CredalNet
- CN Inference
  - CNMonteCarloSampling
  - CNLoopyPropagation
Markov random field
- Undirected Graphical Model
  - MarkovRandomField
- Inference in Markov random fields
  - Shafer-Shenoy Inference in Markov random field
    - ShaferShenoyMRFInference
Probabilistic Relational Models
- PRMexplorer

5- pyAgrum's (experimentals) models

6- pyagrum.lib modules

7- pyAgrum's tools

8- Customizing pyAgrum

9- Appendices

pyagrum

Python Module Index

p


	p
	`pyagrum`
	`pyagrum.causal.notebook`
	`pyagrum.ctbn.StatsIndepTest`

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